A Deep Neural Network Based Holistic Approach for Optical Character Recognition of Handwritten Documents

Optical Character Recognition of handwritten document has been a research topic for last few decades now. Different type of classification schemes starting from template matching, structural analysis to deep neural network have been proposed by researchers. In this paper, a novel holistic approach is proposed for recognition of handwritten words. The approach is a hybrid model combining CNN and BLSTM layers which are responsible for extraction of spatial and temporal features respectively from the word images. Both the features are combined by compact bi-liner pooling. The CBP layer highlights the fine grained details which in turn help to achieve high recognition accuracy. The extracted feature is recognized by a connectionist temporal classification layer. The weights are learnt based on the database using the back propagation algorithm. The hybrid model is trained using three public databases CMATERdb2.1.2, IIIT-HW-Dev and IIIT-HW-Telugu containing Bengali, Devanagari and Telegu handwritten words respectively. The proposed model has achieved 96.42%, 94.79%, 95.07% accuracy on the Bengali, Devanagari and Telegu databases respectively.